# Towards Low-Latency and Ultra-Reliable Virtual Reality

arXiv: https://arxiv.org/abs/1801.07587 · HTML: https://mlsanity.com/paper/1801.07587/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2018.

## Built on by (most cited first, 18 of 18)

- [SimpleEgo: Predicting Probabilistic Body Pose from Egocentric Cameras](https://mlsanity.com/paper/2401.14785/) (2024)
- [Meta-Reinforcement Learning for Reliable Communication in THz/VLC Wireless VR Networks](https://mlsanity.com/paper/2102.12277/) (2021)
- [Human-Centric Resource Allocation for the Metaverse With Multiaccess Edge Computing](https://mlsanity.com/paper/2312.15313/) (2023)
- [WiserVR: Semantic Communication Enabled Wireless Virtual Reality Delivery](https://mlsanity.com/paper/2211.01241/) (2022)
- [Metarobotics for Industry and Society: Vision, Technologies, and Opportunities](https://mlsanity.com/paper/2404.00797/) (2024)
- [Spatio-Temporal Distortion Aware Omnidirectional Video Super-Resolution](https://mlsanity.com/paper/2410.11506/) (2024)
- [Short-Term Trajectory Prediction for Full-Immersive Multiuser Virtual Reality with Redirected Walking](https://mlsanity.com/paper/2207.07520/) (2022)
- [A Hybrid Reinforcement Learning Framework for Hard Latency Constrained Resource Scheduling](https://mlsanity.com/paper/2504.03721/) (2025)
- [Omnidirectional Video Super-Resolution using Deep Learning](https://mlsanity.com/paper/2506.14803/) (2025)
- [Hierarchical JEPA Meets Predictive Remote Control in Beyond 5G Networks](https://mlsanity.com/paper/2602.07000/) (2026)
- [Generational Frameshifts in Technology: Computer Science and Neurosurgery, The VR Use Case](https://mlsanity.com/paper/2110.15719/) (2021)
- [Unified, User and Task (UUT) Centered Artificial Intelligence for Metaverse Edge Computing](https://mlsanity.com/paper/2212.09295/) (2022)
- [ML-powered KQI estimation for XR services. A case study on 360-Video](https://mlsanity.com/paper/2212.12002/) (2022)
- [Predictive Context-Awareness for Full-Immersive Multiuser Virtual Reality with Redirected Walking](https://mlsanity.com/paper/2303.17907/) (2023)
- [Generating Realistic Synthetic Head Rotation Data for Extended Reality using Deep Learning](https://mlsanity.com/paper/2501.09050/) (2025)
- [ASL360: AI-Enabled Adaptive Streaming of Layered 360^circ Video over UAV-assisted Wireless Networks](https://mlsanity.com/paper/2509.10544/) (2025)
- [ETHEREAL: A 25.6-μs/inf. Low-latency Event-driven Graph-neural-network Processor for High-resolution Vision at the Edge](https://mlsanity.com/paper/2608.17787/) (2026)
- [ResLearn-XR: Residual Learning for Network Traffic and Quality-of-Experience-Aware Modeling in Extended Reality](https://mlsanity.com/paper/2609.04493/) (2026)

## Abstract (arXiv)

Virtual Reality (VR) is expected to be one of the killer-applications in 5G networks. However, many technical bottlenecks and challenges need to be overcome to facilitate its wide adoption. In particular, VR requirements in terms of high-throughput, low-latency and reliable communication call for innovative solutions and fundamental research cutting across several disciplines. In view of this, this article discusses the challenges and enablers for ultra-reliable and low-latency VR. Furthermore, in an interactive VR gaming arcade case study, we show that a smart network design that leverages the use of mmWave communication, edge computing and proactive caching can achieve the future vision of VR over wireless.